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Smartwatch User Authentication Based on the Arm-Raising Gesture
Interacting with Computers ( IF 1.0 ) Pub Date : 2021-04-24 , DOI: 10.1093/iwcomp/iwab013
Yanchao Zhao 1 , Ran Gao 1 , Huawei Tu 2
Affiliation  

Smartwatches have arguably become a popular wearable device nowadays. It is important to protect privacy data stored in smartwatches from being stolen. This study proposes a novel smartwatch user authentication technique based on the arm-raising gesture, which is the process of moving the arm from one side of the body to the chest height. We conducted two experiments to verify the effectiveness of the proposed technique. In Experiment 1, we investigated the performance of identifying users with the arm-raising gesture. We selected a set of features and applied them to five basic machine learning algorithms (i.e. random forest, simple logistic, naive Bayes, multilayer perceptron and linear classifier). Results with 32 participants show that with combined features, these classifiers generally achieved high authentication accuracy with high true accept rate (TAR) ($\geq $92.1% for random forest, simple logistic and multilayer perceptron), low false accept rate (FAR) ($\leq $0.6%) and large area under the curve (AUC) of receiver operating characteristics) ($\geq $92.4%). In Experiment 2, we examined the performance of identifying the arm-raising gesture across different day-to-day gestures. Results show that the arm-raising gesture can be identified from other eight common gestures with high TAR ($\geq $99.5%), low FAR ($\leq $3.6%) and large AUC ($\geq $99%). Overall, the results indicate that our technique could be a viable alternative for smartwatch user authentication.

中文翻译:

基于抬手手势的智能手表用户认证

智能手表可以说已经成为当今流行的可穿戴设备。保护存储在智能手表中的隐私数据不被盗很重要。本研究提出了一种基于抬臂手势的新型智能手表用户身份验证技术,该手势是将手臂从身体一侧移动到胸部高度的过程。我们进行了两个实验来验证所提出技术的有效性。在实验 1 中,我们研究了用举手手势识别用户的性能。我们选择了一组特征并将它们应用于五种基本的机器学习算法(即随机森林、简单逻辑、朴素贝叶斯、多层感知器和线性分类器)。32 名参与者的结果表明,结合特征,这些分类器通常以高真实接受率 (TAR) ($\geq $92.1% 对于随机森林、简单逻辑和多层感知器)、低错误接受率 (FAR) ($\leq $0.6%) 和大面积受试者工作特征曲线 (AUC) ($\geq $92.4%)。在实验 2 中,我们检查了在不同日常手势中识别举起手臂手势的性能。结果表明,可以从其他 8 个常见手势中识别出举臂手势,这些手势具有高 TAR($\geq $99.5%)、低 FAR($\leq $3.6%)和大 AUC($\geq $99%)。总体而言,结果表明我们的技术可能是智能手表用户身份验证的可行替代方案。6%)和接受者操作特征曲线下面积大(AUC)($\geq $92.4%)。在实验 2 中,我们检查了在不同日常手势中识别举起手臂手势的性能。结果表明,可以从其他 8 个常见手势中识别出举臂手势,这些手势具有高 TAR($\geq $99.5%)、低 FAR($\leq $3.6%)和大 AUC($\geq $99%)。总体而言,结果表明我们的技术可能是智能手表用户身份验证的可行替代方案。6%)和接受者操作特征曲线下面积大(AUC)($\geq $92.4%)。在实验 2 中,我们检查了在不同日常手势中识别举起手臂手势的性能。结果表明,可以从其他 8 个常见手势中识别出举臂手势,这些手势具有高 TAR($\geq $99.5%)、低 FAR($\leq $3.6%)和大 AUC($\geq $99%)。总体而言,结果表明我们的技术可能是智能手表用户身份验证的可行替代方案。6%)和较大的 AUC($\geq $99%)。总体而言,结果表明我们的技术可能是智能手表用户身份验证的可行替代方案。6%)和较大的 AUC($\geq $99%)。总体而言,结果表明我们的技术可能是智能手表用户身份验证的可行替代方案。
更新日期:2021-04-24
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